Parallel computing for option pricing based on the backward stochastic differential equation

Authors:
Ying Peng;Bin Gong;Hui Liu;Yanxin Zhang
Affiliations:
School of Computer Science and Technology, Shandong University, Jinan, P.R. China;School of Computer Science and Technology, Shandong University, Jinan, P.R. China;School of Computer Science and Technology, Shandong University, Jinan, P.R. China;School of Computer Science and Technology, Shandong University, Jinan, P.R. China
Venue:
HPCA'09 Proceedings of the Second international conference on High Performance Computing and Applications
Year:
2009

Citing 4
Cited 1

Architecture independent parallel binomial tree option price valuations

Parallel Computing
Parallel Algorithm for Pricing American Asian Options with Multi-Dimensional Assets

HPCS '05 Proceedings of the 19th International Symposium on High Performance Computing Systems and Applications
Parallel computing in Asian option pricing

Parallel Computing
A parallel quasi-Monte Carlo approach to pricing multidimensional American options

International Journal of High Performance Computing and Networking

Parallel binomial valuation of american options with proportional transaction costs

APPT'11 Proceedings of the 9th international conference on Advanced parallel processing technologies

Quantified Score

Hi-index	0.00

Visualization

Abstract

The Backward Stochastic Differential Equation (BSDE) is a robust tool for financial derivatives pricing and risk management. In this paper, we explore the opportunity for parallel computing with BSDEs in financial engineering. A binomial tree based numerical method for BSDEs is investigated and applied to option pricing. According to the special structure of the numerical model, we develop a block allocation algorithm in parallelization, where large communication overhead is avoided. Runtime experiments manifest optimistic speedups for the parallel implementation.